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A Transparency Index Framework for AI in Education

Project Overview

The document explores the role of generative AI in education, emphasizing the need for transparency in its development and implementation. It introduces a Transparency Index framework tailored for AI-powered educational technologies, which aims to ensure ethical practices by outlining clear guidelines for data processing, algorithmic transparency, and implementation transparency. Developed through a co-design methodology with input from educators, ed-tech experts, and AI practitioners, the framework highlights that transparency is vital for understanding AI systems and building trust among users. Additionally, it addresses ethical issues surrounding fairness, accountability, and safety in AI applications. The findings suggest that by prioritizing transparency, educational institutions can better leverage generative AI to enhance learning experiences while mitigating potential risks, ultimately fostering a more ethical and effective integration of AI technologies in the educational landscape.

Key Applications

Transparency Index framework for AI in education

Context: Educational technology development for schools, targeting educators, ed-tech experts, and AI practitioners.

Implementation: Developed through a co-design methodology involving interviews and feedback from various educational stakeholders.

Outcomes: Enhanced understanding of AI-powered products among educators and improved documentation processes for AI practitioners.

Challenges: Initial lack of awareness and understanding of transparency among stakeholders; potential cognitive overload from too much information.

Implementation Barriers

Awareness Barrier

Many stakeholders, including educators, are not aware of the importance of transparency in AI systems, ethical considerations in AI products, and the need for education on these topics.

Proposed Solutions: Education and training on AI transparency and ethical AI practices for all stakeholders involved in the use of AI in education.

Communication Barrier

There is a gap in communication between AI practitioners and educators regarding transparency needs and ethical AI practices.

Proposed Solutions: Development of frameworks like the Transparency Index to facilitate better understanding and communication of AI product functionalities.

Information Overload Barrier

Providing too much information to stakeholders can lead to cognitive overload, making it difficult for them to discern relevant details.

Proposed Solutions: Implementing tiered transparency levels, where information is tailored to the specific needs and understanding of different stakeholder groups.

Project Team

Muhammad Ali Chaudhry

Researcher

Mutlu Cukurova

Researcher

Rose Luckin

Researcher

Contact Information

For information about the paper, please contact the authors.

Authors: Muhammad Ali Chaudhry, Mutlu Cukurova, Rose Luckin

Source Publication: View Original PaperLink opens in a new window

Project Contact: Dr. Jianhua Yang

LLM Model Version: gpt-4o-mini-2024-07-18